SURVEY ON PANDEMIC OUTBREAK PREDICTION METHODS
Keywords:
Temporal Pattern Recognition, Autoregressive Integrated Moving Average, Support Vector Machine, Transform Transformer.Abstract
The emergence of COVID-19 in 2019 led to a global pandemic, causing significant damage to society, politics, and the economy, with millions of lives lost. Accurately forecasting COVID-19's future spread is critical in managing its impact. This study evaluates the performance of four time-series analysis models - ARIMA, Prophet, LSTM, and Transformer models - to predict future COVID-19 trends in six nations. We obtain COVID-19 case data from the publicly available database of Johns Hopkins University Center for Systems Science and Engineering and use it to make predictions repeatedly across all models. Performance evaluation is done using mean squared error (MSE) and mean absolute error (MAE). Our findings show that the LSTM model achieves the lowest MSE and MAE across all countries. Although the Transformer model performs poorly overall, it has the second-best performances in certain countries. These results highlight the high accuracy of the LSTM model in forecasting the spread of COVID-19, enabling countries to better plan and implement measures to control the virus.
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